Similarity is a strange notion: it’s the same, but different. You probably don’t lose any sleep over it, but how could a deterministic algorithm deal with it?
This presentation on Wed, Dec 9, at 12.00-13.00 CET, will show how you can build deep neural networks that quantify similarity between images, and allow downstream tasks like content search or data clustering. We’ll also get an understanding for how the data is represented in the networks, and why this works in the first place.
Does ”similar” actually have a very specific meaning to you? There are many techniques out there to fine-tune networks for similarity, whether you have labeled ground truth examples or not.
About the speaker
Romain Futrzynski is a technical communicator at Peltarion. He has several years of experience working in Computational Fluid Dynamics, notably to ensure customer success of simulation engineers at Siemens Digital Industries Software. Romain is passionate about computer science and deep learning, and about sharing knowledge between all branches of AI and Engineering. He has a PhD in Mechanical Engineering from KTH.